进化计算在行动：面向千兆像素病理图像深度嵌入空间的特征选择
Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images
AI总结：
本文提出基于大规模多目标优化的进化特征选择方法，通过频繁特征直方图构建紧凑的全切片图像表示，在TCGA数据上以约14,000倍压缩实现8%的准确率提升，并引入新的决策空间和可视化方法。
摘要：
采用数字病理学的主要障碍之一是高效处理高维数字化活检样本（称为全切片图像，WSIs）的挑战。迫切需要利用深度学习并引入紧凑的WSI表示，以加速图像分析，并促进后疫情时代病理结果的可视化和可解释性。本文提出了一种新的基于深度嵌入的大规模多目标优化（LSMOP）的WSI表示进化方法。我们首先采用基于补丁的采样来输入KimiaNet（一个组织病理学专用的深度网络），并提取大量特征向量。粗粒度多目标特征选择使用由分类精度和特征数量引导的缩减搜索空间策略。在第二阶段，通过多次运行粗粒度LSMOP构建频繁特征直方图（FFH），这是一种新颖的WSI表示。然后应用细粒度进化特征选择，基于FFH找到紧凑（短长度）的特征向量，并在进化算法随机能力的支持下，为数字病理学提供更稳健的深度学习方法。我们使用癌症基因组图谱（TCGA）图像在WSI表示、分类精度和特征质量方面验证了所提出的方案。此外，在LSMOP领域引入了一种新的多准则决策空间。最后，提出了一种补丁级可视化方法，以提高深度特征的可解释性。所提出的进化算法找到了一个非常紧凑的特征向量来表示WSI（比原始特征向量小约14,000倍），与最先进方法提供的编码相比，准确率提高了8%。
英文摘要：
One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exploiting deep learning and introducing compact WSI representations are urgently needed to accelerate image analysis and facilitate the visualization and interpretability of pathology results in a postpandemic world. In this paper, we introduce a new evolutionary approach for WSI representation based on large-scale multi-objective optimization (LSMOP) of deep embeddings. We start with patch-based sampling to feed KimiaNet , a histopathology-specialized deep network, and to extract a multitude of feature vectors. Coarse multi-objective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features. In the second stage, the frequent features histogram (FFH), a novel WSI representation, is constructed by multiple runs of coarse LSMOP. Fine evolutionary feature selection is then applied to find a compact (short-length) feature vector based on the FFH and contributes to a more robust deep-learning approach to digital pathology supported by the stochastic power of evolutionary algorithms. We validate the proposed schemes using The Cancer Genome Atlas (TCGA) images in terms of WSI representation, classification accuracy, and feature quality. Furthermore, a novel decision space for multicriteria decision making in the LSMOP field is introduced. Finally, a patch-level visualization approach is proposed to increase the interpretability of deep features. The proposed evolutionary algorithm finds a very compact feature vector to represent a WSI (almost 14,000 times smaller than the original feature vectors) with 8% higher accuracy compared to the codes provided by the state-of-the-art methods.
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